(1) Field of Invention
The present invention relates to a system for object detection for video sequences and, more particularly, to a system for object detection for video sequences that is able to adapt to changing environments via an online learning scheme.
(2) Description of Related Art
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (e.g., humans, cars) in digital images and videos. In order to detect a certain class of objects, a classifier first must be trained to recognize the class of objects using a set of training samples.
Online training gives a system the ability to adapt to a changing environment. Previous work in online learning requires streams of human labeled data in real time (or simulated real time), as described by Tzotsos and Argialas in “Support Vector Machine Classification for Object-Based Image Analysis”, Lecture Notes in Geoinformation and Cartography, 2008, pp. 663-677, which is hereby incorporated by reference as though fully set forth herein. While it is preferable to use human labels for online training, in practice this is rare, if not impossible, to come by. It is labor intensive, and if running real-time, may be physically impossible.
Thus, a continuing need exists for an object detection system that is able to adapt to changing environments via an online learning scheme that uses a trained classifier to perform the task of labeling data, removing the dependency on human annotations.